Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations1140
Missing cells564
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory601.1 KiB
Average record size in memory539.9 B

Variable types

URL1
Categorical17
Numeric6
DateTime1
Text1

Alerts

Domain length is highly overall correlated with Number of lettersHigh correlation
Indication of young domain is highly overall correlated with LabelHigh correlation
Issuer organization is highly overall correlated with Label and 2 other fieldsHigh correlation
Label is highly overall correlated with Indication of young domain and 4 other fieldsHigh correlation
Number of dots (.) is highly overall correlated with Presence of prefix 'www' High correlation
Number of letters is highly overall correlated with Domain lengthHigh correlation
Presence in the standard Tranco list is highly overall correlated with Tranco List rankHigh correlation
Presence of SiteJabber reviews is highly overall correlated with SSL certificate issuerHigh correlation
Presence of TrustPilot reviews is highly overall correlated with TrustPilot scoreHigh correlation
Presence of credit card payment is highly overall correlated with Presence of money back paymentHigh correlation
Presence of free contact emails is highly overall correlated with LabelHigh correlation
Presence of money back payment is highly overall correlated with Presence of credit card paymentHigh correlation
Presence of prefix 'www' is highly overall correlated with Number of dots (.)High correlation
SSL certificate issuer is highly overall correlated with Issuer organization and 3 other fieldsHigh correlation
SSL certificate issuer organization list item is highly overall correlated with Issuer organization and 2 other fieldsHigh correlation
Tranco List rank is highly overall correlated with Presence in the standard Tranco listHigh correlation
TrustPilot score is highly overall correlated with Presence of TrustPilot reviewsHigh correlation
Number of digits is highly imbalanced (87.8%) Imbalance
Number of hyphens (-) is highly imbalanced (67.1%) Imbalance
Presence of crypto currency is highly imbalanced (78.1%) Imbalance
Presence of SiteJabber reviews is highly imbalanced (76.4%) Imbalance
Presence in the standard Tranco list is highly imbalanced (88.3%) Imbalance
TrustPilot score has 560 (49.1%) missing values Missing
Online shop URL has unique values Unique

Reproduction

Analysis started2025-03-13 06:32:37.226332
Analysis finished2025-03-13 06:33:25.662521
Duration48.44 seconds
Software versionydata-profiling vv4.14.0
Download configurationconfig.json

Variables

Online shop URL
URL

Unique 

Distinct1140
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size93.3 KiB
https://www.ghingwala.shop
 
1
https://www.allaccessorybest.com
 
1
https://www.b-watches.shop
 
1
https://www.waeschenamen-windrath.de
 
1
https://www.scandinavian-lifestyle.de
 
1
Other values (1135)
1135 
ValueCountFrequency (%)
https://www.ghingwala.shop 1
 
0.1%
https://www.allaccessorybest.com 1
 
0.1%
https://www.b-watches.shop 1
 
0.1%
https://www.waeschenamen-windrath.de 1
 
0.1%
https://www.scandinavian-lifestyle.de 1
 
0.1%
https://patakhay.com 1
 
0.1%
https://www.chasin.com 1
 
0.1%
https://www.emporiumcosplay.com 1
 
0.1%
https://store.dji.com/l 1
 
0.1%
https://www.vynokerai.lt 1
 
0.1%
Other values (1130) 1130
99.1%
ValueCountFrequency (%)
https 1138
99.8%
http 2
 
0.2%
ValueCountFrequency (%)
www.ghingwala.shop 1
 
0.1%
www.allaccessorybest.com 1
 
0.1%
www.b-watches.shop 1
 
0.1%
www.waeschenamen-windrath.de 1
 
0.1%
www.scandinavian-lifestyle.de 1
 
0.1%
patakhay.com 1
 
0.1%
www.chasin.com 1
 
0.1%
www.emporiumcosplay.com 1
 
0.1%
store.dji.com 1
 
0.1%
www.vynokerai.lt 1
 
0.1%
Other values (1130) 1130
99.1%
ValueCountFrequency (%)
1052
92.3%
/en 36
 
3.2%
/lt 10
 
0.9%
/e 7
 
0.6%
/it 2
 
0.2%
/es 2
 
0.2%
/de 2
 
0.2%
/pt 2
 
0.2%
/gb 2
 
0.2%
/nl-nl 1
 
0.1%
Other values (24) 24
 
2.1%
ValueCountFrequency (%)
1140
100.0%
ValueCountFrequency (%)
1140
100.0%

Label
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
fraudulent
579 
legitimate
561 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters11400
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfraudulent
2nd rowfraudulent
3rd rowlegitimate
4th rowlegitimate
5th rowfraudulent

Common Values

ValueCountFrequency (%)
fraudulent 579
50.8%
legitimate 561
49.2%

Length

2025-03-13T14:33:25.820552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T14:33:25.884068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fraudulent 579
50.8%
legitimate 561
49.2%

Most occurring characters

ValueCountFrequency (%)
t 1701
14.9%
e 1701
14.9%
u 1158
10.2%
a 1140
10.0%
l 1140
10.0%
i 1122
9.8%
f 579
 
5.1%
r 579
 
5.1%
n 579
 
5.1%
d 579
 
5.1%
Other values (2) 1122
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11400
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1701
14.9%
e 1701
14.9%
u 1158
10.2%
a 1140
10.0%
l 1140
10.0%
i 1122
9.8%
f 579
 
5.1%
r 579
 
5.1%
n 579
 
5.1%
d 579
 
5.1%
Other values (2) 1122
9.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 11400
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1701
14.9%
e 1701
14.9%
u 1158
10.2%
a 1140
10.0%
l 1140
10.0%
i 1122
9.8%
f 579
 
5.1%
r 579
 
5.1%
n 579
 
5.1%
d 579
 
5.1%
Other values (2) 1122
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 1701
14.9%
e 1701
14.9%
u 1158
10.2%
a 1140
10.0%
l 1140
10.0%
i 1122
9.8%
f 579
 
5.1%
r 579
 
5.1%
n 579
 
5.1%
d 579
 
5.1%
Other values (2) 1122
9.8%

Domain length
Real number (ℝ)

High correlation 

Distinct28
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.341228
Minimum7
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2025-03-13T14:33:25.962293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile11
Q115
median19
Q321
95-th percentile25
Maximum38
Range31
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.5120327
Coefficient of variation (CV)0.24600494
Kurtosis0.16076836
Mean18.341228
Median Absolute Deviation (MAD)3
Skewness0.081635699
Sum20909
Variance20.358439
MonotonicityNot monotonic
2025-03-13T14:33:26.062115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
20 109
 
9.6%
19 106
 
9.3%
18 100
 
8.8%
17 89
 
7.8%
21 83
 
7.3%
16 74
 
6.5%
22 74
 
6.5%
23 72
 
6.3%
14 68
 
6.0%
15 65
 
5.7%
Other values (18) 300
26.3%
ValueCountFrequency (%)
7 5
 
0.4%
8 7
 
0.6%
9 14
 
1.2%
10 25
 
2.2%
11 35
3.1%
12 32
2.8%
13 53
4.6%
14 68
6.0%
15 65
5.7%
16 74
6.5%
ValueCountFrequency (%)
38 1
 
0.1%
34 1
 
0.1%
32 4
 
0.4%
31 2
 
0.2%
30 3
 
0.3%
29 5
 
0.4%
28 5
 
0.4%
27 16
1.4%
26 15
1.3%
25 32
2.8%

Top domain length
Real number (ℝ)

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8517544
Minimum2
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2025-03-13T14:33:26.237798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum13
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.74132175
Coefficient of variation (CV)0.25995287
Kurtosis33.721838
Mean2.8517544
Median Absolute Deviation (MAD)0
Skewness3.3658237
Sum3251
Variance0.54955794
MonotonicityNot monotonic
2025-03-13T14:33:26.317027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 772
67.7%
2 300
 
26.3%
5 32
 
2.8%
4 24
 
2.1%
6 11
 
1.0%
13 1
 
0.1%
ValueCountFrequency (%)
2 300
 
26.3%
3 772
67.7%
4 24
 
2.1%
5 32
 
2.8%
6 11
 
1.0%
13 1
 
0.1%
ValueCountFrequency (%)
13 1
 
0.1%
6 11
 
1.0%
5 32
 
2.8%
4 24
 
2.1%
3 772
67.7%
2 300
 
26.3%

Presence of prefix 'www'
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
1
759 
0
381 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1140
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 759
66.6%
0 381
33.4%

Length

2025-03-13T14:33:26.385336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T14:33:26.463535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 759
66.6%
0 381
33.4%

Most occurring characters

ValueCountFrequency (%)
1 759
66.6%
0 381
33.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1140
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 759
66.6%
0 381
33.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1140
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 759
66.6%
0 381
33.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 759
66.6%
0 381
33.4%

Number of digits
Categorical

Imbalance 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
0
1099 
2
 
19
1
 
13
3
 
7
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1140
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1099
96.4%
2 19
 
1.7%
1 13
 
1.1%
3 7
 
0.6%
4 2
 
0.2%

Length

2025-03-13T14:33:26.547931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T14:33:26.616615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1099
96.4%
2 19
 
1.7%
1 13
 
1.1%
3 7
 
0.6%
4 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1099
96.4%
2 19
 
1.7%
1 13
 
1.1%
3 7
 
0.6%
4 2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1140
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1099
96.4%
2 19
 
1.7%
1 13
 
1.1%
3 7
 
0.6%
4 2
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1140
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1099
96.4%
2 19
 
1.7%
1 13
 
1.1%
3 7
 
0.6%
4 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1099
96.4%
2 19
 
1.7%
1 13
 
1.1%
3 7
 
0.6%
4 2
 
0.2%

Number of letters
Real number (ℝ)

High correlation 

Distinct27
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.654386
Minimum11
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2025-03-13T14:33:26.704946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile15
Q119
median22
Q324.25
95-th percentile29
Maximum39
Range28
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation4.307723
Coefficient of variation (CV)0.19893074
Kurtosis0.083297317
Mean21.654386
Median Absolute Deviation (MAD)3
Skewness0.13715386
Sum24686
Variance18.556478
MonotonicityNot monotonic
2025-03-13T14:33:26.785926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
22 120
10.5%
23 116
10.2%
20 97
 
8.5%
21 93
 
8.2%
24 83
 
7.3%
26 76
 
6.7%
18 72
 
6.3%
25 71
 
6.2%
17 70
 
6.1%
19 68
 
6.0%
Other values (17) 274
24.0%
ValueCountFrequency (%)
11 7
 
0.6%
12 6
 
0.5%
13 15
 
1.3%
14 27
 
2.4%
15 37
 
3.2%
16 44
3.9%
17 70
6.1%
18 72
6.3%
19 68
6.0%
20 97
8.5%
ValueCountFrequency (%)
39 1
 
0.1%
37 1
 
0.1%
35 1
 
0.1%
34 2
 
0.2%
33 7
 
0.6%
32 6
 
0.5%
31 7
 
0.6%
30 16
1.4%
29 19
1.7%
28 27
2.4%

Number of dots (.)
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
2
764 
1
354 
3
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1140
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 764
67.0%
1 354
31.1%
3 22
 
1.9%

Length

2025-03-13T14:33:26.886258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T14:33:26.959627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 764
67.0%
1 354
31.1%
3 22
 
1.9%

Most occurring characters

ValueCountFrequency (%)
2 764
67.0%
1 354
31.1%
3 22
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1140
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 764
67.0%
1 354
31.1%
3 22
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1140
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 764
67.0%
1 354
31.1%
3 22
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 764
67.0%
1 354
31.1%
3 22
 
1.9%

Number of hyphens (-)
Categorical

Imbalance 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
0
974 
1
154 
2
 
11
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1140
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 974
85.4%
1 154
 
13.5%
2 11
 
1.0%
4 1
 
0.1%

Length

2025-03-13T14:33:27.040806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T14:33:27.102393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 974
85.4%
1 154
 
13.5%
2 11
 
1.0%
4 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 974
85.4%
1 154
 
13.5%
2 11
 
1.0%
4 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1140
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 974
85.4%
1 154
 
13.5%
2 11
 
1.0%
4 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1140
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 974
85.4%
1 154
 
13.5%
2 11
 
1.0%
4 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 974
85.4%
1 154
 
13.5%
2 11
 
1.0%
4 1
 
0.1%

Presence of credit card payment
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
1
870 
0
270 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1140
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 870
76.3%
0 270
 
23.7%

Length

2025-03-13T14:33:27.186643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T14:33:27.260223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 870
76.3%
0 270
 
23.7%

Most occurring characters

ValueCountFrequency (%)
1 870
76.3%
0 270
 
23.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1140
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 870
76.3%
0 270
 
23.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1140
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 870
76.3%
0 270
 
23.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 870
76.3%
0 270
 
23.7%

Presence of money back payment
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
1
813 
0
327 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1140
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 813
71.3%
0 327
28.7%

Length

2025-03-13T14:33:27.338720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T14:33:27.407319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 813
71.3%
0 327
28.7%

Most occurring characters

ValueCountFrequency (%)
1 813
71.3%
0 327
28.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1140
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 813
71.3%
0 327
28.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1140
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 813
71.3%
0 327
28.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 813
71.3%
0 327
28.7%

Presence of cash on delivery payment
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
0
868 
1
272 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1140
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 868
76.1%
1 272
 
23.9%

Length

2025-03-13T14:33:27.487229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T14:33:27.559322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 868
76.1%
1 272
 
23.9%

Most occurring characters

ValueCountFrequency (%)
0 868
76.1%
1 272
 
23.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1140
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 868
76.1%
1 272
 
23.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1140
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 868
76.1%
1 272
 
23.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 868
76.1%
1 272
 
23.9%

Presence of crypto currency
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
0
1100 
1
 
40

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1140
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1100
96.5%
1 40
 
3.5%

Length

2025-03-13T14:33:27.641434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T14:33:27.710316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1100
96.5%
1 40
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 1100
96.5%
1 40
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1140
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1100
96.5%
1 40
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1140
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1100
96.5%
1 40
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1100
96.5%
1 40
 
3.5%

Presence of free contact emails
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
0
567 
2
406 
3
111 
1
 
56

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1140
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row2
4th row2
5th row0

Common Values

ValueCountFrequency (%)
0 567
49.7%
2 406
35.6%
3 111
 
9.7%
1 56
 
4.9%

Length

2025-03-13T14:33:27.787554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T14:33:27.870521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 567
49.7%
2 406
35.6%
3 111
 
9.7%
1 56
 
4.9%

Most occurring characters

ValueCountFrequency (%)
0 567
49.7%
2 406
35.6%
3 111
 
9.7%
1 56
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1140
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 567
49.7%
2 406
35.6%
3 111
 
9.7%
1 56
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1140
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 567
49.7%
2 406
35.6%
3 111
 
9.7%
1 56
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 567
49.7%
2 406
35.6%
3 111
 
9.7%
1 56
 
4.9%

Presence of logo URL
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
1
937 
0
203 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1140
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 937
82.2%
0 203
 
17.8%

Length

2025-03-13T14:33:27.951039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T14:33:28.023212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 937
82.2%
0 203
 
17.8%

Most occurring characters

ValueCountFrequency (%)
1 937
82.2%
0 203
 
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1140
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 937
82.2%
0 203
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1140
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 937
82.2%
0 203
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 937
82.2%
0 203
 
17.8%

SSL certificate issuer
Categorical

High correlation 

Distinct43
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size76.2 KiB
R3
411 
GTS CA 1P5
329 
E1
129 
Cloudflare Inc ECC CA-3
87 
Sectigo RSA Domain Validation Secure Server CA
42 
Other values (38)
142 

Length

Max length53
Median length52
Mean length11.305263
Min length2

Characters and Unicode

Total characters12888
Distinct characters56
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)1.2%

Sample

1st rowGTS CA 1P5
2nd rowCloudflare Inc ECC CA-3
3rd rowR3
4th rowGTS CA 1P5
5th rowE1

Common Values

ValueCountFrequency (%)
R3 411
36.1%
GTS CA 1P5 329
28.9%
E1 129
 
11.3%
Cloudflare Inc ECC CA-3 87
 
7.6%
Sectigo RSA Domain Validation Secure Server CA 42
 
3.7%
cPanel, Inc. Certification Authority 13
 
1.1%
DigiCert TLS RSA SHA256 2020 CA1 12
 
1.1%
Amazon RSA 2048 M02 10
 
0.9%
Certum Domain Validation CA SHA2 9
 
0.8%
Sectigo RSA Extended Validation Secure Server CA 9
 
0.8%
Other values (33) 89
 
7.8%

Length

2025-03-13T14:33:28.107271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 446
15.1%
r3 413
14.0%
gts 330
 
11.1%
1p5 329
 
11.1%
e1 129
 
4.4%
rsa 117
 
4.0%
inc 100
 
3.4%
ecc 91
 
3.1%
ca-3 87
 
2.9%
cloudflare 87
 
2.9%
Other values (64) 831
28.1%

Most occurring characters

ValueCountFrequency (%)
1820
 
14.1%
C 878
 
6.8%
S 782
 
6.1%
A 771
 
6.0%
e 559
 
4.3%
R 541
 
4.2%
1 517
 
4.0%
3 513
 
4.0%
i 416
 
3.2%
r 413
 
3.2%
Other values (46) 5678
44.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4817
37.4%
Lowercase Letter 4335
33.6%
Space Separator 1820
 
14.1%
Decimal Number 1772
 
13.7%
Dash Punctuation 115
 
0.9%
Other Punctuation 29
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 559
12.9%
i 416
9.6%
r 413
9.5%
a 392
9.0%
o 391
9.0%
n 329
7.6%
l 306
7.1%
t 299
 
6.9%
c 267
 
6.2%
u 202
 
4.7%
Other values (14) 761
17.6%
Uppercase Letter
ValueCountFrequency (%)
C 878
18.2%
S 782
16.2%
A 771
16.0%
R 541
11.2%
G 406
8.4%
T 394
8.2%
P 345
 
7.2%
E 261
 
5.4%
I 100
 
2.1%
D 99
 
2.1%
Other values (8) 240
 
5.0%
Decimal Number
ValueCountFrequency (%)
1 517
29.2%
3 513
29.0%
5 358
20.2%
2 167
 
9.4%
0 102
 
5.8%
8 38
 
2.1%
6 36
 
2.0%
4 34
 
1.9%
9 7
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 15
51.7%
, 13
44.8%
/ 1
 
3.4%
Space Separator
ValueCountFrequency (%)
1820
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 115
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9152
71.0%
Common 3736
29.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 878
 
9.6%
S 782
 
8.5%
A 771
 
8.4%
e 559
 
6.1%
R 541
 
5.9%
i 416
 
4.5%
r 413
 
4.5%
G 406
 
4.4%
T 394
 
4.3%
a 392
 
4.3%
Other values (32) 3600
39.3%
Common
ValueCountFrequency (%)
1820
48.7%
1 517
 
13.8%
3 513
 
13.7%
5 358
 
9.6%
2 167
 
4.5%
- 115
 
3.1%
0 102
 
2.7%
8 38
 
1.0%
6 36
 
1.0%
4 34
 
0.9%
Other values (4) 36
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1820
 
14.1%
C 878
 
6.8%
S 782
 
6.1%
A 771
 
6.0%
e 559
 
4.3%
R 541
 
4.2%
1 517
 
4.0%
3 513
 
4.0%
i 416
 
3.2%
r 413
 
3.2%
Other values (46) 5678
44.1%

SSL certificate expire date
Date

Distinct1065
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
Minimum2023-08-05 15:32:45+00:00
Maximum2024-09-17 23:59:59+00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-13T14:33:28.200372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:28.301800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Issuer organization
Categorical

High correlation 

Distinct18
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size82.4 KiB
Let's Encrypt
540 
Google Trust Services LLC
330 
Cloudflare, Inc.
87 
Sectigo Limited
56 
DigiCert Inc
55 
Other values (13)
72 

Length

Max length30
Median length28
Mean length16.85614
Min length6

Characters and Unicode

Total characters19216
Distinct characters42
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.4%

Sample

1st rowGoogle Trust Services LLC
2nd rowCloudflare, Inc.
3rd rowLet's Encrypt
4th rowGoogle Trust Services LLC
5th rowLet's Encrypt

Common Values

ValueCountFrequency (%)
Let's Encrypt 540
47.4%
Google Trust Services LLC 330
28.9%
Cloudflare, Inc. 87
 
7.6%
Sectigo Limited 56
 
4.9%
DigiCert Inc 55
 
4.8%
Amazon 18
 
1.6%
cPanel, Inc. 13
 
1.1%
GlobalSign nv-sa 12
 
1.1%
Unizeto Technologies S.A. 10
 
0.9%
ZeroSSL 4
 
0.4%
Other values (8) 15
 
1.3%

Length

2025-03-13T14:33:28.401071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
let's 540
18.4%
encrypt 540
18.4%
trust 331
11.3%
google 330
11.2%
services 330
11.2%
llc 330
11.2%
inc 163
 
5.6%
cloudflare 87
 
3.0%
sectigo 56
 
1.9%
limited 56
 
1.9%
Other values (23) 173
 
5.9%

Most occurring characters

ValueCountFrequency (%)
e 1856
 
9.7%
1796
 
9.3%
t 1594
 
8.3%
r 1364
 
7.1%
L 1262
 
6.6%
s 1231
 
6.4%
c 1128
 
5.9%
o 893
 
4.6%
n 784
 
4.1%
i 652
 
3.4%
Other values (32) 6656
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12931
67.3%
Uppercase Letter 3685
 
19.2%
Space Separator 1796
 
9.3%
Other Punctuation 792
 
4.1%
Dash Punctuation 12
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1856
14.4%
t 1594
12.3%
r 1364
10.5%
s 1231
9.5%
c 1128
8.7%
o 893
 
6.9%
n 784
 
6.1%
i 652
 
5.0%
l 562
 
4.3%
y 544
 
4.2%
Other values (12) 2323
18.0%
Uppercase Letter
ValueCountFrequency (%)
L 1262
34.2%
E 540
14.7%
C 474
 
12.9%
S 426
 
11.6%
G 346
 
9.4%
T 346
 
9.4%
I 164
 
4.5%
D 62
 
1.7%
A 33
 
0.9%
P 15
 
0.4%
Other values (5) 17
 
0.5%
Other Punctuation
ValueCountFrequency (%)
' 540
68.2%
. 143
 
18.1%
, 109
 
13.8%
Space Separator
ValueCountFrequency (%)
1796
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16616
86.5%
Common 2600
 
13.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1856
 
11.2%
t 1594
 
9.6%
r 1364
 
8.2%
L 1262
 
7.6%
s 1231
 
7.4%
c 1128
 
6.8%
o 893
 
5.4%
n 784
 
4.7%
i 652
 
3.9%
l 562
 
3.4%
Other values (27) 5290
31.8%
Common
ValueCountFrequency (%)
1796
69.1%
' 540
 
20.8%
. 143
 
5.5%
, 109
 
4.2%
- 12
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1856
 
9.7%
1796
 
9.3%
t 1594
 
8.3%
r 1364
 
7.1%
L 1262
 
6.6%
s 1231
 
6.4%
c 1128
 
5.9%
o 893
 
4.6%
n 784
 
4.1%
i 652
 
3.4%
Other values (32) 6656
34.6%

SSL certificate issuer organization list item
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5701754
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2025-03-13T14:33:28.501868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q39
95-th percentile9
Maximum11
Range10
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.3364702
Coefficient of variation (CV)0.73005298
Kurtosis-1.5610981
Mean4.5701754
Median Absolute Deviation (MAD)1
Skewness0.52190789
Sum5210
Variance11.132033
MonotonicityNot monotonic
2025-03-13T14:33:28.588193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 543
47.6%
9 330
28.9%
1 87
 
7.6%
3 56
 
4.9%
7 55
 
4.8%
6 18
 
1.6%
11 18
 
1.6%
4 13
 
1.1%
8 12
 
1.1%
10 4
 
0.4%
ValueCountFrequency (%)
1 87
 
7.6%
2 543
47.6%
3 56
 
4.9%
4 13
 
1.1%
5 4
 
0.4%
6 18
 
1.6%
7 55
 
4.8%
8 12
 
1.1%
9 330
28.9%
10 4
 
0.4%
ValueCountFrequency (%)
11 18
 
1.6%
10 4
 
0.4%
9 330
28.9%
8 12
 
1.1%
7 55
 
4.8%
6 18
 
1.6%
5 4
 
0.4%
4 13
 
1.1%
3 56
 
4.9%
2 543
47.6%

Indication of young domain
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
0
550 
1
473 
2
117 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1140
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 550
48.2%
1 473
41.5%
2 117
 
10.3%

Length

2025-03-13T14:33:28.688486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T14:33:28.767734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 550
48.2%
1 473
41.5%
2 117
 
10.3%

Most occurring characters

ValueCountFrequency (%)
0 550
48.2%
1 473
41.5%
2 117
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1140
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 550
48.2%
1 473
41.5%
2 117
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1140
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 550
48.2%
1 473
41.5%
2 117
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 550
48.2%
1 473
41.5%
2 117
 
10.3%

Domain registration date
Text

Distinct943
Distinct (%)83.0%
Missing4
Missing (%)0.4%
Memory size80.0 KiB
2025-03-13T14:33:28.904255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length16
Median length16
Mean length14.901408
Min length6

Characters and Unicode

Total characters16928
Distinct characters19
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique901 ?
Unique (%)79.3%

Sample

1st row2023-05-15 03:35
2nd row2023-06-18 05:43
3rd rowHidden
4th row2022-09-20 00:00
5th row2023-07-27 09:05
ValueCountFrequency (%)
00:00 315
 
14.7%
hidden 117
 
5.5%
05:00 25
 
1.2%
04:00 15
 
0.7%
2023-05-15 15
 
0.7%
2023-05-17 13
 
0.6%
2023-05-08 10
 
0.5%
2023-07-20 8
 
0.4%
2023-07-19 8
 
0.4%
2023-05-11 8
 
0.4%
Other values (1188) 1608
75.1%
2025-03-13T14:33:29.126445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4445
26.3%
2 2359
13.9%
- 2036
12.0%
1 1680
 
9.9%
: 1006
 
5.9%
1006
 
5.9%
3 948
 
5.6%
5 591
 
3.5%
9 481
 
2.8%
6 445
 
2.6%
Other values (9) 1931
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12176
71.9%
Dash Punctuation 2036
 
12.0%
Other Punctuation 1008
 
6.0%
Space Separator 1006
 
5.9%
Lowercase Letter 585
 
3.5%
Uppercase Letter 117
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4445
36.5%
2 2359
19.4%
1 1680
 
13.8%
3 948
 
7.8%
5 591
 
4.9%
9 481
 
4.0%
6 445
 
3.7%
4 436
 
3.6%
7 422
 
3.5%
8 369
 
3.0%
Lowercase Letter
ValueCountFrequency (%)
d 234
40.0%
i 117
20.0%
e 117
20.0%
n 117
20.0%
Other Punctuation
ValueCountFrequency (%)
: 1006
99.8%
. 2
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 2036
100.0%
Space Separator
ValueCountFrequency (%)
1006
100.0%
Uppercase Letter
ValueCountFrequency (%)
H 117
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16226
95.9%
Latin 702
 
4.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4445
27.4%
2 2359
14.5%
- 2036
12.5%
1 1680
 
10.4%
: 1006
 
6.2%
1006
 
6.2%
3 948
 
5.8%
5 591
 
3.6%
9 481
 
3.0%
6 445
 
2.7%
Other values (4) 1229
 
7.6%
Latin
ValueCountFrequency (%)
d 234
33.3%
H 117
16.7%
i 117
16.7%
e 117
16.7%
n 117
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16928
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4445
26.3%
2 2359
13.9%
- 2036
12.0%
1 1680
 
9.9%
: 1006
 
5.9%
1006
 
5.9%
3 948
 
5.6%
5 591
 
3.5%
9 481
 
2.8%
6 445
 
2.6%
Other values (9) 1931
11.4%

Presence of TrustPilot reviews
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
0
885 
1
255 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1140
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 885
77.6%
1 255
 
22.4%

Length

2025-03-13T14:33:29.222940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T14:33:29.291251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 885
77.6%
1 255
 
22.4%

Most occurring characters

ValueCountFrequency (%)
0 885
77.6%
1 255
 
22.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1140
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 885
77.6%
1 255
 
22.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1140
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 885
77.6%
1 255
 
22.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 885
77.6%
1 255
 
22.4%

TrustPilot score
Real number (ℝ)

High correlation  Missing 

Distinct39
Distinct (%)6.7%
Missing560
Missing (%)49.1%
Infinite0
Infinite (%)0.0%
Mean1.0148276
Minimum-1
Maximum5
Zeros0
Zeros (%)0.0%
Negative325
Negative (%)28.5%
Memory size9.0 KiB
2025-03-13T14:33:29.369772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q33.6
95-th percentile4.8
Maximum5
Range6
Interquartile range (IQR)4.6

Descriptive statistics

Standard deviation2.3637418
Coefficient of variation (CV)2.3292053
Kurtosis-1.6163545
Mean1.0148276
Median Absolute Deviation (MAD)0
Skewness0.43911961
Sum588.6
Variance5.5872754
MonotonicityNot monotonic
2025-03-13T14:33:29.468282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
-1 325
28.5%
3.2 18
 
1.6%
3.7 18
 
1.6%
4.8 18
 
1.6%
3.8 16
 
1.4%
4.4 13
 
1.1%
4.7 13
 
1.1%
4.9 11
 
1.0%
4.1 11
 
1.0%
3.9 11
 
1.0%
Other values (29) 126
 
11.1%
(Missing) 560
49.1%
ValueCountFrequency (%)
-1 325
28.5%
1 1
 
0.1%
1.3 1
 
0.1%
1.4 3
 
0.3%
1.5 3
 
0.3%
1.6 2
 
0.2%
1.7 3
 
0.3%
1.8 4
 
0.4%
1.9 2
 
0.2%
2 1
 
0.1%
ValueCountFrequency (%)
5 3
 
0.3%
4.9 11
1.0%
4.8 18
1.6%
4.7 13
1.1%
4.6 5
 
0.4%
4.5 5
 
0.4%
4.4 13
1.1%
4.3 5
 
0.4%
4.2 5
 
0.4%
4.1 11
1.0%

Presence of SiteJabber reviews
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
0
1096 
1
 
44

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1140
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1096
96.1%
1 44
 
3.9%

Length

2025-03-13T14:33:29.561700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T14:33:29.631550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1096
96.1%
1 44
 
3.9%

Most occurring characters

ValueCountFrequency (%)
0 1096
96.1%
1 44
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1140
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1096
96.1%
1 44
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1140
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1096
96.1%
1 44
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1096
96.1%
1 44
 
3.9%

Presence in the standard Tranco list
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
0
1122 
1
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1140
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1122
98.4%
1 18
 
1.6%

Length

2025-03-13T14:33:29.707355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-13T14:33:29.763832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1122
98.4%
1 18
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 1122
98.4%
1 18
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1140
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1122
98.4%
1 18
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1140
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1122
98.4%
1 18
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1122
98.4%
1 18
 
1.6%

Tranco List rank
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9710.7711
Minimum-1
Maximum964101
Zeros0
Zeros (%)0.0%
Negative1122
Negative (%)98.4%
Memory size9.0 KiB
2025-03-13T14:33:29.846525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile-1
Maximum964101
Range964102
Interquartile range (IQR)0

Descriptive statistics

Standard deviation84946.86
Coefficient of variation (CV)8.7476946
Kurtosis84.438454
Mean9710.7711
Median Absolute Deviation (MAD)0
Skewness9.1504368
Sum11070279
Variance7.215969 × 109
MonotonicityNot monotonic
2025-03-13T14:33:29.944933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
-1 1122
98.4%
90036 1
 
0.1%
802243 1
 
0.1%
804620 1
 
0.1%
35556 1
 
0.1%
459343 1
 
0.1%
825217 1
 
0.1%
802653 1
 
0.1%
682488 1
 
0.1%
538286 1
 
0.1%
Other values (9) 9
 
0.8%
ValueCountFrequency (%)
-1 1122
98.4%
35556 1
 
0.1%
90036 1
 
0.1%
100803 1
 
0.1%
346606 1
 
0.1%
459343 1
 
0.1%
481570 1
 
0.1%
538286 1
 
0.1%
682488 1
 
0.1%
750712 1
 
0.1%
ValueCountFrequency (%)
964101 1
0.1%
918108 1
0.1%
875089 1
0.1%
825217 1
0.1%
804620 1
0.1%
802653 1
0.1%
802243 1
0.1%
797093 1
0.1%
796877 1
0.1%
750712 1
0.1%

Interactions

2025-03-13T14:33:24.872225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:22.447527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:22.940820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:23.432961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:23.911804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:24.381460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:24.943877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:22.532387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:23.019527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:23.514748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:23.980806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:24.475270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:25.036406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:22.614079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:23.098563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:23.582811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:24.064696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:24.543838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:25.112725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:22.693897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:23.179075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:23.665834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:24.148520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:24.632066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:25.197353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:22.778337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:23.241623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:23.742449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:24.230738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:24.712067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:25.286757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:22.856475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:23.331403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:23.835630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:24.313045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-13T14:33:24.781036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-13T14:33:30.038542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Domain lengthIndication of young domainIssuer organizationLabelNumber of digitsNumber of dots (.)Number of hyphens (-)Number of lettersPresence in the standard Tranco listPresence of SiteJabber reviewsPresence of TrustPilot reviewsPresence of cash on delivery paymentPresence of credit card paymentPresence of crypto currencyPresence of free contact emailsPresence of logo URLPresence of money back paymentPresence of prefix 'www'SSL certificate issuerSSL certificate issuer organization list itemTop domain lengthTranco List rankTrustPilot score
Domain length1.0000.2910.1260.3930.1270.3870.1060.9620.1280.0000.1810.0570.0740.0000.2080.0620.1190.4870.1620.1920.289-0.1400.068
Indication of young domain0.2911.0000.3870.8080.0470.1700.0690.2620.1110.1850.4350.2300.1380.0720.3960.2990.1510.2280.4690.3850.0000.0700.085
Issuer organization0.1260.3871.0000.5380.0000.3270.1290.0930.1010.3780.4090.2270.2030.0000.3270.1770.2380.3310.9890.9910.1610.0460.087
Label0.3930.8080.5381.0000.0000.1730.0500.3400.1030.1880.4620.2730.0000.0000.6060.3490.0210.1570.6290.5370.0840.0740.319
Number of digits0.1270.0470.0000.0001.0000.0380.1510.1270.0000.0000.0000.0230.0820.0640.0000.0000.0930.0550.1890.0340.0000.0000.000
Number of dots (.)0.3870.1700.3270.1730.0381.0000.0000.3430.1840.2070.1640.0870.1790.0250.1950.0330.1700.9470.3890.2810.4320.0980.183
Number of hyphens (-)0.1060.0690.1290.0500.1510.0001.0000.0650.0000.0470.0000.0320.0000.0140.0490.0000.0000.0000.1760.1290.0000.0000.057
Number of letters0.9620.2620.0930.3400.1270.3430.0651.0000.1360.0000.1200.0220.0620.0000.2010.0560.1130.4070.1250.1660.271-0.1300.084
Presence in the standard Tranco list0.1280.1110.1010.1030.0000.1840.0000.1361.0000.0980.1400.0440.0000.0000.0660.0400.0000.1690.2440.1140.0000.9390.092
Presence of SiteJabber reviews0.0000.1850.3780.1880.0000.2070.0470.0000.0981.0000.3010.1140.0000.0000.0760.0690.0000.0730.5460.3770.1800.2450.322
Presence of TrustPilot reviews0.1810.4350.4090.4620.0000.1640.0000.1200.1400.3011.0000.2040.0000.0430.2840.2070.0820.0000.4550.3930.0630.1370.995
Presence of cash on delivery payment0.0570.2300.2270.2730.0230.0870.0320.0220.0440.1140.2041.0000.1520.0600.1820.1250.1120.0400.2650.1920.0000.0800.061
Presence of credit card payment0.0740.1380.2030.0000.0820.1790.0000.0620.0000.0000.0000.1521.0000.0000.0810.0000.5930.1600.2580.1920.0890.0000.049
Presence of crypto currency0.0000.0720.0000.0000.0640.0250.0140.0000.0000.0000.0430.0600.0001.0000.0340.0000.0440.0110.0000.0000.0000.0000.000
Presence of free contact emails0.2080.3960.3270.6060.0000.1950.0490.2010.0660.0760.2840.1820.0810.0341.0000.1800.0820.2820.4340.3140.1080.0520.094
Presence of logo URL0.0620.2990.1770.3490.0000.0330.0000.0560.0400.0690.2070.1250.0000.0000.1801.0000.0490.0410.1250.1810.1120.0000.092
Presence of money back payment0.1190.1510.2380.0210.0930.1700.0000.1130.0000.0000.0820.1120.5930.0440.0820.0491.0000.1630.2700.2180.0760.0000.131
Presence of prefix 'www'0.4870.2280.3310.1570.0550.9470.0000.4070.1690.0730.0000.0400.1600.0110.2820.0410.1631.0000.4030.3060.0290.1490.171
SSL certificate issuer0.1620.4690.9890.6290.1890.3890.1760.1250.2440.5460.4550.2650.2580.0000.4340.1250.2700.4031.0000.9800.1840.1660.171
SSL certificate issuer organization list item0.1920.3850.9910.5370.0340.2810.1290.1660.1140.3770.3930.1920.1920.0000.3140.1810.2180.3060.9801.0000.110-0.0550.078
Top domain length0.2890.0000.1610.0840.0000.4320.0000.2710.0000.1800.0630.0000.0890.0000.1080.1120.0760.0290.1840.1101.000-0.0850.126
Tranco List rank-0.1400.0700.0460.0740.0000.0980.000-0.1300.9390.2450.1370.0800.0000.0000.0520.0000.0000.1490.166-0.055-0.0851.0000.111
TrustPilot score0.0680.0850.0870.3190.0000.1830.0570.0840.0920.3220.9950.0610.0490.0000.0940.0920.1310.1710.1710.0780.1260.1111.000

Missing values

2025-03-13T14:33:25.398053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-13T14:33:25.514002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-13T14:33:25.630157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Online shop URLLabelDomain lengthTop domain lengthPresence of prefix 'www'Number of digitsNumber of lettersNumber of dots (.)Number of hyphens (-)Presence of credit card paymentPresence of money back paymentPresence of cash on delivery paymentPresence of crypto currencyPresence of free contact emailsPresence of logo URLSSL certificate issuerSSL certificate expire dateIssuer organizationSSL certificate issuer organization list itemIndication of young domainDomain registration datePresence of TrustPilot reviewsTrustPilot scorePresence of SiteJabber reviewsPresence in the standard Tranco listTranco List rank
0https://www.allaccessorybest.comfraudulent243102720110000GTS CA 1P5Oct 11 03:53:36 2023 GMTGoogle Trust Services LLC912023-05-15 03:350NaN00-1
1https://www.b-watches.shopfraudulent184102021010010Cloudflare Inc ECC CA-3Jun 16 23:59:59 2024 GMTCloudflare, Inc.112023-06-18 05:430NaN00-1
2https://www.waeschenamen-windrath.delegitimate282103021100021R3Oct 24 21:53:20 2023 GMTLet's Encrypt22Hidden0-1.000-1
3https://vendoprint.selegitimate132001710100021GTS CA 1P5Oct 9 15:13:00 2023 GMTGoogle Trust Services LLC912022-09-20 00:000-1.000-1
4https://www.newbikeland.comfraudulent193102220110001E1Oct 25 08:20:27 2023 GMTLet's Encrypt212023-07-27 09:050NaN00-1
5https://www.hats-storeofficial.comfraudulent263102821111001GTS CA 1P5Sep 13 07:55:33 2023 GMTGoogle Trust Services LLC912023-06-15 08:460NaN00-1
6https://balaganstudio.comlegitimate173002110111021R3Oct 16 21:18:12 2023 GMTLet's Encrypt202015-04-16 00:2213.200-1
7https://www.bootscleaner.comlegitimate203102320110021R3Oct 11 08:36:31 2023 GMTLet's Encrypt212022-09-14 00:0013.700-1
8https://shop.tovarseasycleaning.comlegitimate273003020110011R3Sep 27 01:10:26 2023 GMTLet's Encrypt202020-11-08 20:100-1.000-1
9https://camerabebelusului.rolegitimate202002410010021R3Sep 2 18:59:11 2023 GMTLet's Encrypt202012-10-23 00:0014.500-1
Online shop URLLabelDomain lengthTop domain lengthPresence of prefix 'www'Number of digitsNumber of lettersNumber of dots (.)Number of hyphens (-)Presence of credit card paymentPresence of money back paymentPresence of cash on delivery paymentPresence of crypto currencyPresence of free contact emailsPresence of logo URLSSL certificate issuerSSL certificate expire dateIssuer organizationSSL certificate issuer organization list itemIndication of young domainDomain registration datePresence of TrustPilot reviewsTrustPilot scorePresence of SiteJabber reviewsPresence in the standard Tranco listTranco List rank
1130https://nikinclothing.comlegitimate173002110110021R3Oct 13 02:28:24 2023 GMTLet's Encrypt202017-02-01 23:4113.900-1
1131https://www.gardentoolhome.comfraudulent223102520110001GTS CA 1P5Oct 2 07:12:37 2023 GMTGoogle Trust Services LLC912023-07-03 00:000NaN00-1
1132https://www.chichomeitem.comfraudulent203102320110031E1Nov 17 09:35:46 2023 GMTLet's Encrypt212023-06-21 06:350NaN00-1
1133https://www.adventuregearup.comfraudulent233102620110001E1Oct 5 03:27:09 2023 GMTLet's Encrypt212023-07-07 00:000NaN00-1
1134https://sportfits.eulegitimate122001610110021R3Oct 21 10:12:10 2023 GMTLet's Encrypt22Hidden13.900-1
1135https://www.tinneystoys.comlegitimate193102820110001GeoTrust Global TLS RSA4096 SHA256 2022 CA1Oct 23 23:59:59 2023 GMTDigiCert Inc702014-05-13 16:1614.800-1
1136https://kalistara.ltlegitimate122001610100000R3Oct 6 21:32:22 2023 GMTLet's Encrypt202015-01-30 00:000-1.000-1
1137https://www.allkindsoftools.comfraudulent233102620110001GTS CA 1P5Sep 3 04:22:33 2023 GMTGoogle Trust Services LLC912023-06-05 00:000NaN00-1
1138https://www.arlton.shopfraudulent154101820110031GTS CA 1P5Oct 29 11:30:40 2023 GMTGoogle Trust Services LLC90NaN0NaN00-1
1139https://www.ghingwala.shopfraudulent184102120001000Cloudflare Inc ECC CA-3Mar 16 23:59:59 2024 GMTCloudflare, Inc.10NaN0NaN00-1